\section{Conclusion}\label{sec:conclude}

We presented a system for generating responses that are directly tied to  responders' agendas and document content.
Our response generation architecture integrates NLP and NLG methods while providing an easy-to-use and easy-to-extend  solution.
To the best of our knowledge, this is the first system to generate {\em subjective} responses directly related to individual agendas.
%Our evaluation method facilitates quantifying human-likeness and scrutinizing  aspects  of human-like response.
We evaluated both the human-likeness and the relevance of  generated content, thereby providing a  set of empirical results on the  efficacy of computer-generated responses  compared head-to-head against human responses. Generating concise, relevant, and opinionated responses that are  human-like is hard -- it   requires the integration of text-understanding, subjectivity  and sentiment analysis,  and  it benefits from capturing  agents'  prior knowledge,  reasons and motives. Our work  is a starting point for ample  research on  generated content that would effectively pass a Turing-like test, and successfully convince humans of the authenticity of the responses.\footnote{Our code, data, analysis scripts, implementation details and raw data (computer and human responses), %as well as the generation code and analysis scripts
will be made publicly available to the research community through \url{www.authors.com} upon acceptance.}
%\section*{Acknowledgments} 